33 research outputs found
Deep Learning for Logo Detection: A Survey
When logos are increasingly created, logo detection has gradually become a
research hotspot across many domains and tasks. Recent advances in this area
are dominated by deep learning-based solutions, where many datasets, learning
strategies, network architectures, etc. have been employed. This paper reviews
the advance in applying deep learning techniques to logo detection. Firstly, we
discuss a comprehensive account of public datasets designed to facilitate
performance evaluation of logo detection algorithms, which tend to be more
diverse, more challenging, and more reflective of real life. Next, we perform
an in-depth analysis of the existing logo detection strategies and the
strengths and weaknesses of each learning strategy. Subsequently, we summarize
the applications of logo detection in various fields, from intelligent
transportation and brand monitoring to copyright and trademark compliance.
Finally, we analyze the potential challenges and present the future directions
for the development of logo detection to complete this survey
RS-Net: robust segmentation of green overlapped apples
Fruit detection and segmentation will be essential for future agronomic management, with applications in yield estimation, growth monitoring, intelligent picking, disease detection and etc. In order to more accurately and efficiently realize the recognition and segmentation of apples in natural orchards, a robust segmentation net framework specially developed for fruit production is proposed. This model was improved for the more challenging problem which segments the overlapped apples from the monochromatic background regardless of various corruptions. The method extends Mask R-CNN by embedding an attention mechanism for focusing more on the informative pixels but also suppressing the noise caused by adverse factors (occlusions, overlaps, etc.), which could be more suitable and robust for operating in complex natural environment. Specifically, the Gaussian non-local attention mechanism is transplanted into Mask R-CNN for refining the semantic features generated continuously by residual network and feature pyramid network, then the model forward processing based on the balanced feature levels and finally segments the regions where the apples are located. Experimental results verify the hypothesis of current work and show that the proposed method outperforms other start-of-the-art detection and segmentation models, the AP box and AP mask metric values have reached 85.6% and 86.2% in a reasonable run time, respectively, which can meet the precision and robustness of vision system in agronomic managemen
Association between sleep duration and quality with rapid kidney function decline and development of chronic kidney diseases in adults with normal kidney function: The China health and retirement longitudinal study
Research have shown that sleep is associated with renal function. However, the potential effects of sleep duration or quality on kidney function in middle-aged and older Chinese adults with normal kidney function has rarely been studied. Our study aimed to investigate the association of sleep and kidney function in middle-aged and older Chinese adults. Four thousand and eighty six participants with an eGFR ā„60 ml/min/1.73 m2 at baseline were enrolled between 2011 and 2015 from the China Health and Retirement Longitudinal Study. Survey questionnaire data were collected from conducted interviews in the 2011. The eGFR was estimated from serum creatinine and/or cystatin C using the Chronic Kidney Disease Epidemiology Collaboration equations (CKD-EPI). The primary outcome was defined as rapid kidney function decline. Secondary outcome was defined as rapid kidney function decline with clinical eGFR of <60 ml/min/1.73 m2 at the exit visit. The associations between sleep duration, sleep quality and renal function decline or chronic kidney disease (CKD) were assessed based with logistic regression model. Our results showed that 244 (6.0%) participants developed rapid decline in kidney function, while 102 (2.5%) developed CKD. In addition, participants who had 3ā7 days of poor sleep quality per week had higher risks of CKD development (OR 1.86, 95% CI 1.24ā2.80). However, compared with those who had 6ā8 h of night-time sleep, no significantly higher risks of rapid decline in kidney function was found among those who had <6 h or >8 h of night time sleep after adjustments for demographic, clinical, or psychosocial covariates. Furthermore, daytime nap did not present significant risk in both rapid eGFR decline or CKD development. In conclusion, sleep quality was significantly associated with the development of CKD in middle-aged and older Chinese adults with normal kidney function
Audio-Visual-Based Query by Example Video Retrieval
Query by example video retrieval aims at automatic retrieval of video samples which are similar to a user-provided example from video database. Considering that much of prior work on video analysis support retrieval using only visual features, in this paper, a two-step method for query by example is proposed, in which both audio and visual features are used. In the proposed method, a set of audio and visual features are, respectively, extracted from the shot level and key frame level. Among these features, audio features are employed to rough retrieval, while visual features are applied to refine retrieval. The experimental results demonstrate the good performance of the proposed approach
Rapid Non-Destructive Analysis of Food Nutrient Content Using Swin-Nutrition
Food non-destructive detection technology (NDDT) is a powerful impetus to the development of food safety and quality. One of the essential tasks of food quality regulation is the non-destructive detection of the food’s nutrient content. However, existing food nutrient NDDT performs poorly in terms of efficiency and accuracy, which hinders their widespread application in daily meals. Therefore, this paper proposed an end-to-end food nutrition non-destructive detection method, named Swin-Nutrition, which combined deep learning and NDDT to evaluate the nutrient content of food. The method aimed to fully capture the feature information from the food images and thus accurately estimate the nutrient content. Swin-Nutrition resorted to Swin Transformer, the feature fusion module (FFM), and the nutrient prediction module to evaluate nutrient content. In particular, Swin Transformer acted as the backbone network for feature extraction of food images, and FFM was used to obtain the discriminative feature representation to improve the accuracy of prediction. The experimental results on the Nutrition5k dataset demonstrated the effectiveness and efficiency of our proposed method. Specifically, the mean value of the percentage mean absolute error (PMAE) for calories, mass, fat, carbohydrate, and protein were only 15.3%, 12.5%, 22.1%, 20.8%, and 15.4%, respectively. We hope that our simple and effective method will provide a solid foundation for the research of food NDDT
Deblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer
Various image preāprocessing tasks in optical coherence tomography (OCT) systems involve reversing degradation effects (e.g. deblurring). Current deblurring research mainly focuses on how to build suitable degradation models using deconvolution operators. However, modelābased solutions may not work well in many scenarios. To solve this problem, the authors propose a nonāmodel architecture, called a deep convolutional neural network, to address parameterāfree situations. The proposed solution employs a deep learning strategy to bridge the gap between traditional modelābased methods and neural network architectures. Experiments on retinal OCT images demonstrate that the proposed approach achieves superior performance compared with the stateāofātheāart modelābased OCT deblurring methods
Using Building Information Modeling to Enhance Supply Chain Resilience in Prefabricated Buildings: A Conceptual Framework
Prefabricated buildings usually involve various project participants and complicated processes of design, manufacturing, transport, assembly, and construction, which means they constantly face supply chain disruptions. As a tool to realize information integration and facilitate communication among project participants in the supply chain, building information modeling (BIM) is widely recognized as an important technology to foster supply chain resilience. However, it is unclear how BIM can facilitate supply chain resilience in prefabricated buildings. This study aims to construct a conceptual framework to better understand the influencing paths of BIM on supply chain resilience in the context of prefabricated buildings. It employs an integrative review method to identify key factors influencing the resilience of the prefabricated building supply chain and explore the effects of BIM on these factors. The role of BIM in linking these factors was verified through an empirical case. The results show that BIM resources and capabilities can enhance supply chain resilience by influencing participant factors (assembly construction capability, design capability) and partnership factors (information sharing, cooperation, coordination, and trust). This study incorporates supply chain resilience and BIM technology into a conceptual framework in the context of prefabricated buildings, providing new theoretical insights for future supply chain management
Deep Hierarchical Representation from Classifying Logo-405
We introduce a logo classification mechanism which combines a series of deep representations obtained by fine-tuning convolutional neural network (CNN) architectures and traditional pattern recognition algorithms. In order to evaluate the proposed mechanism, we build a middle-scale logo dataset (named Logo-405) and treat it as a benchmark for logo related research. Our experiments are carried out on both the Logo-405 dataset and the publicly available FlickrLogos-32 dataset. The experimental results demonstrate that the proposed mechanism outperforms two popular ways used for logo classification, including the strategies that integrate hand-crafted features and traditional pattern recognition algorithms and the models which employ deep CNNs